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Gene expression profiling predicts clinical outcome of breast cancer

Nature volume 415, pages 530–536 (2002)Cite this article

Abstract

Breast cancer patients with the same stage of disease can have markedly different treatment responses and overall outcome. The strongest predictors for metastases (for example, lymph node status and histological grade) fail to classify accurately breast tumours according to their clinical behaviour1,2,3. Chemotherapy or hormonal therapy reduces the risk of distant metastases by approximately one-third; however, 70–80% of patients receiving this treatment would have survived without it4,5. None of the signatures of breast cancer gene expression reported to date6,7,8,9,10,11,12 allow for patient-tailored therapy strategies. Here we used DNA microarray analysis on primary breast tumours of 117 young patients, and applied supervised classification to identify a gene expression signature strongly predictive of a short interval to distant metastases (‘poor prognosis’ signature) in patients without tumour cells in local lymph nodes at diagnosis (lymph node negative). In addition, we established a signature that identifies tumours of BRCA1 carriers. The poor prognosis signature consists of genes regulating cell cycle, invasion, metastasis and angiogenesis. This gene expression profile will outperform all currently used clinical parameters in predicting disease outcome. Our findings provide a strategy to select patients who would benefit from adjuvant therapy.

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Figure 1: Unsupervised two-dimensional cluster analysis of 98 breast tumours.
Figure 2: Supervised classification on prognosis signatures.
Figure 3: Supervised classification on ER and BRCA1 signatures.

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Acknowledgements

We thank D. Atsma and D. Majoor for assistance with the histological analyses and the preparation of tumour RNA; T. van der Velde, W. van Waardenburg and O. Dalesio for medical record data extraction; D. Slade, J. McDonald, J. Koch, T. Erkkila, M. Parrish and others at Rosetta's High Throughput Gene Expression Profiling Facility for microarray experiments; R. Stoughton, F. van Leeuwen, M. Rookus, P. Nederlof, F. Hogervorst and D. Voskuil for suggestions; and A. Berns, L. Hartwell, J. Radich and S. Rodenhuis for support and reading of the manuscript. This work was supported by a grant from the Center for Biomedical Genetics.

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Author notes

  1. Laura J. van 't Veer, Hongyue Dai, Marc J. van de Vijver, Yudong D. He, Mao Mao, Matthew J. Marton, George J. Schreiber, Chris Roberts, Peter S. Linsley and Stephen H. Friend: These authors contributed equally to this work

Authors and Affiliations

  1. Divisions of Diagnostic Oncology, Radiotherapy and Molecular Carcinogenesis and Center for Biomedical Genetics, The Netherlands Cancer Institute, 121 Plesmanlaan, Amsterdam, 1066 CX, The Netherlands

    Laura J. van 't Veer, Marc J. van de Vijver, Augustinus A. M. Hart, Hans L. Peterse, Karin van der Kooy, Anke T. Witteveen, Ron M. Kerkhoven & René Bernards

  2. Rosetta Inpharmatics, 12040 115th Avenue NE, Kirkland, 98034, Washington, USA

    Hongyue Dai, Yudong D. He, Mao Mao, Matthew J. Marton, George J. Schreiber, Chris Roberts, Peter S. Linsley & Stephen H. Friend

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  1. Laura J. van 't Veer
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Corresponding author

Correspondence to Stephen H. Friend.

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Competing interests

S.H.F. is the Vice President of the MRC Merck Research Laboratories.

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van 't Veer, L., Dai, H., van de Vijver, M. et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530–536 (2002). https://doi.org/10.1038/415530a

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